Foundations of Machine Learning Frameworks  

CSCN8010 - Winter 2024  

Professor: Ran Feldesh  

Student: Arcadio de Paula Fernandez

1. Graph using Matplotlib

To create the graph below, we will use the plotting library for Python called Matplotlib. As data, we will use the classic Titanic database, containing the number of passengers, age, sex, survivors, etc.

For more information about the Titanic you can access the following link.

The graph is a histogram showing the distribution of the number of passengers and their age.

# Importing several libraries for data visualization
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# Loading the dataset in seaborn data repository of Titanic
df = sns.load_dataset('titanic')

df.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

# The 'age' column was selected and missing values were dropped by using .dropna()
ages = df['age'].dropna()

#The number of bins were set and to create the histogram
n_bins = 30

# Creating the histogram plot
plt.hist(ages, bins=n_bins, edgecolor="white")


# Setting labels and title
plt.xlabel('Age')
plt.ylabel('Number of Passengers')
plt.title('Histogram of Passenger Ages')

# Showing the plot
plt.show()

2. Graph using Seaborn

The graph below is another histogram showing the distribution of the number of passengers and their age, but now in Seaborn, also a Python data visualization library.

import seaborn as sns

# Loading the dataset in seaborn data repository of Titanic
df = sns.load_dataset('titanic')

df.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

plt.figure(figsize=(10, 6))
sns.histplot(data=df, x='age', kde=True, hue='sex')
plt.title('Age Distribution by Gender')
plt.show()

3. Graph using Plotly

The graphs below show the number of passengers that survived and died but are now in Plotly Express, also a Python data visualization library.

# Loading the dataset in seaborn data repository of Titanic and saving it in the 
titanic_data = sns.load_dataset('titanic')

# Viewing the first 5 rows
titanic_data.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

import plotly.express as px
import plotly.offline as pyo

pyo.init_notebook_mode()


#plotly.offline.init_notebook_mode()

fig = px.pie(titanic_data, names='survived', title='Passenger Survival',color_discrete_map={'Not Survived': 'red', 'Survived': 'green'},labels={'SurvivalLabel': 'Survival'})
fig.show()

fig = px.scatter(titanic_data, x='fare', y='age', color='survived', size='fare')
fig.show()

Converting your notebook into an HTML:

!jupyter nbconvert --to html "C:\Users\arcad\OneDrive\Área de Trabalho\02_Machine Learning Engineer\09_Conestoga_Applied AI_ML\01_Term 1_Conestoga_Subjects\02_Foundations ML_Ran\00_ML_Lab_Sandbox\CSCN8010-labs-Class 2\Class 2_Lab_Arcadio_v3.ipynb" --output-dir ./docs/